scholarly journals Precision Analysis of Poisson Control Chart Based on Sample Size

2020 ◽  
Vol 16 (3) ◽  
pp. 325
Author(s):  
Elsa Resa Sari
Keyword(s):  
Quality Control ◽  
Sample Size ◽  
Control Chart ◽  
Average Run Length ◽  
Statistical Quality Control ◽  
Sample Sizes ◽  
Precision Analysis ◽  
Run Length ◽  
Statistical Quality ◽  
Result Show

One technique used in performing statistical quality control is by poisson control chart. Poisson control chart used in data that have the same mean and varians for monitoring the number of defects in the study. In some cases, the different sample sizes influence the control chart performance. The control chart performance can be measured using average run length (ARL). The smaller ARL’s value, the better type of control chart. In this study, we used different sample sizes  that is  and mean . The result show the best performance of control chart is when  and m = 200, because its has a smaller ARL’s value.                            

scholarly journals A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Saad T. Bakir
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Control Charts ◽  
Probability Distributions ◽  
Average Run Length ◽  
Training Sample ◽  
Statistical Quality Control ◽  
Test Statistic ◽  
Run Length ◽  
Quality Control Chart

This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.

On the Estimation Error in Zero-Inflated Poisson Model for Process Control

2003 ◽  
Vol 10 (02) ◽  
pp. 159-169 ◽  
Author(s):  
B. He ◽  
M. Xie ◽  
T. N. Goh ◽  
P. Ranjan
Keyword(s):  
Sample Size ◽  
Poisson Distribution ◽  
Control Chart ◽  
Average Run Length ◽  
Estimation Error ◽  
Accurate Estimate ◽  
Minimum Size ◽  
High Quality ◽  
Run Length ◽  
Run Length Distribution

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

scholarly journals A new alternative Hotelling’s T2 control chart using MEWMA to monitor minimal mean deviation

2021 ◽  
Vol 336 ◽  
pp. 09021
Author(s):  
Kunyun Wang ◽  
Qianqian Li ◽  
Guangdong Li
Keyword(s):  
Production Process ◽  
Control Chart ◽  
Average Run Length ◽  
Quality Characteristics ◽  
Mean Deviation ◽  
Sample Sizes ◽  
Forgetting Factor ◽  
Good Efficiency ◽  
Run Length ◽  
T2 Control Chart

Hotelling T2 control chart not only reflects the correla-tions between different quality characteristics but also has good efficiency on monitoring multivariate quality characteristics in production process. A new alternative control chart was constructed after the original products data are processed by using multivariate exponentially weighted moving average for cumulating failure effects because T2 control chart is ineffective on detecting minimal mean deviations. Exemplified by bivariate quality characteristics, we compared the monitoring effects of Hotelling T2 control chart and new control chart which is called as T2MEWMA control chart. Paper showed the improved T2MEWMA control chart has smaller average run length than Hotelling T2 control chart on monitoring minimal mean deviation and that also studied the relationships between T2MEWMA control chart’s forgetting factor, sample sizes N and type II error. It indicated the smaller forgetting factor is more sensitive to minimal mean value deviation and that average run length tended to become bigger gradually along with increase of sample sizes N when production process is out of control.

scholarly journals PENGENDALIAN KUALITAS DENGAN MENGGUNAKAN METODE STATISTICAL QUALITY CONTROL (SQC) UNTUK MEMINIMUMKAN PRODUK GAGAL PADA TOKO ROTI BAROKAH BAKERY

2017 ◽  
Vol 7 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Muhammad Syarif Hidayatullah Elmas
Keyword(s):  
Quality Control ◽  
Human Factors ◽  
Control Chart ◽  
Control Charts ◽  
Field Research ◽  
Statistical Quality Control ◽  
Analysis Tool ◽  
Statistical Quality ◽  
Cause And Effect ◽  
Library Research

Quality Control is an activity (company management) to maintain and navigate to product quality (and) services companies can be maintained as planned. Quality Control used the company to minimize failed products using methods of Statistical Quality Control (SQC) so that the company can meet the quality products that have been specified businesses and consumers are satisfied in consuming the product. The purpose of that will be achieved in this research is to determine the method of Statistical Quality Control (SQC) with tekhnik control chart and diagram of cause and effect applied the company Bakery Barokah Bakery in quality control to minimize the product failed. Tekhnik data collection that is used is a library research and field research, while data analysis tool  that  is  used control  chart and  diagram  of  cause  and  effect. The  results  of  the  analysis  of control charts shows that the number of products that are examined as much as 27.710 units, the average of 0,099 product damage or 9.9%. Limitations: UCL supervision of 0,1161 or 11.61%, LCL of 0,0819 or 8,12%. Quality control on  the Bakery Barokah Bakery is  good because the  number of failed products are  still within the boundaries of fair is located between UCL and LCL. While the results from the diagram for result (fish bones), the main factor the causes of the failure of the products of bread in the Bakery Barokah Bakery namely human factors. Where people fail in the making of the bread products. So required training to minimize failed products that occurred caused by human factors.

Modified chart with optimized ARLs in autocorrelated processes

2015 ◽  
Vol 27 (5) ◽  
pp. 633-647 ◽  
Author(s):  
D. R. Prajapati ◽  
Sukhraj Singh
Keyword(s):  
Quality Control ◽  
Sample Size ◽  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Service Industries ◽  
Content Type ◽  
Autocorrelated Processes ◽  
Warning Limits ◽  
And Shifts

Purpose – The purpose of this paper is to counter autocorrelation by designing the chart, using warning limits. Various optimal schemes of modified chart are proposed for various sample sizes (n) at levels of correlation (Φ) of 0.00, 0.475 and 0.95. These optimal schemes of modified chart are compared with the double sampling (DS) chart, suggested by Costa and Claro (2008). Design/methodology/approach – The performance of the chart is measured in terms of the average run length (ARL) that is the average number of samples before getting an out-of-control signal. Ultimately, due to the effect of autocorrelation among the data, the performance of the chart is suspected. The ARLs at various sets of parameters of the chart are computed by simulation, using MATLAB. The suggested optimal schemes are simpler schemes with limited number of parameters and smaller sample size (n=4) and this simplicity makes them very helpful in quality control. Findings – The suggested optimal schemes of modified chart are compared with the DS chart, suggested by Costa and Claro (2008). It is concluded that the modified chart outperforms the DS chart at various levels of correlation (Φ) and shifts in the process mean. The simplicity in the design of modified chart, makes it versatile for many industries. Research limitations/implications – Both the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for chart can be developed for variable sample size and for variable sampling intervals. The optimal schemes can also be explored for cumulative sum and exponentially weighted moving average charts. Practical implications – The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. The understandable and robust design of modified chart makes it usable for industrial quality control. Social implications – The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi (1985). Originality/value – Although it is the extension of previous work but it can be applied to various manufacturing industries as well as service industries, where the data are positively correlated and normally distributed.

scholarly journals Minimize Defects in 5 Liters Jerry Cans by Using Statistical Quality Control and Root Cause Analysis

2021 ◽  
Vol 4 (2) ◽  
pp. 51-63
Author(s):  
Bayu Bisma Nanda ◽  
Wiwik Sulistiyowati
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Chart ◽  
Root Cause Analysis ◽  
Statistical Quality Control ◽  
Cause Analysis ◽  
Household Appliances ◽  
Cumulative Percentage ◽  
Statistical Quality ◽  
Root Cause

PT. DS2 is one of the producers of household appliances, kitchen utensils, and children's toys to bottles and jerry cans, to produce 5 liters of jerry using 2 Blow Molding machines that can produce an average jerry of 6750 / plastic jerry per day. In the production process there are defects found in 5 liter jerry products. This study aims to determine the cause of defect, what factors cause defect. Method used in this research is SQC and RCA in an effort to determine the defect in the 5 liter jerry production process. From the results of research that has been done, the highest value of disability is PL bloom defect with 1686 pcs with a cumulative percentage of 41%. Based on the analysis using the control p chart that all percentages of defective pruduct are still in the control chart and declared safe.

scholarly journals Monitoring The Level Of Students GPA's Over Time

2010 ◽  
Vol 3 (6) ◽  
pp. 43-50
Author(s):  
Saad T. Bakir ◽  
Bob McNeal
Keyword(s):  
False Alarm ◽  
Normal Distribution ◽  
False Alarm Rate ◽  
Control Chart ◽  
Average Run Length ◽  
Statistical Quality Control ◽  
Grade Point ◽  
Run Length ◽  
College Of Business ◽  
Over Time

A nonparametric (or distribution-free) statistical quality control chart is used to monitor the cumulative grade point averages (GPAs) of students over time. The chart is designed to detect any statistically significant positive or negative shifts in student GPAs from a desired target level. This nonparametric control chart is based on the signed-ranks of the GPAs of the sampled students. The exact false alarm rate and the in-control average run length of the proposed chart can be computed exactly and are independent of the underlying probability distribution of GPAs. The traditional Shewhart X-bar control chart for monitoring the mean of a process is based on the assumption that data follows a normal distribution. However, student GPAs may differ significantly from the normal distribution. As a result, using a traditional control chart to monitor the GPAs of students may lead to incorrectly specifying the control limits and the average run length and/or the false alarm rate of the chart. A test study was conducted at the College of Business Administration at Alabama State University. The study monitored the median cumulative GPAs of management majors during the period Spring 2005 through Spring 2009. The study revealed that the GPAs of students were stable at a median level of 2.6 over the period of the study.

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